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Climate Change, Adaptation and Formal Education: The Role of Schooling for Increasing Societies' Adaptive Capacities
Wamsler, Christine
2011
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Wamsler, C. (2011). Climate Change, Adaptation and Formal Education: The Role of Schooling for Increasing Societies' Adaptive Capacities. (Summary of the outcomes of the project component b of the EU project
‘Forecasting Societies’ Adaptive Capacities to Climate Change’; Vol. IIASA Interim Report IR-11-024).
International Institute for Applied Systems Analysis. http://www.iiasa.ac.at/publication/more_IR-11-024.php Total number of authors:
1
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Interim Report IR-11-024
Climate Change, Adaptation, and Formal Education:
The Role of Schooling for Increasing Societies’
Adaptive Capacities
Christine Wamsler
with contributions from Ebba Brink, Pasi Oskari Rantala and Mercedes Barillas
Approved by Wolfgang Lutz
Leader, World Population Program July 6, 2011
Contents
1 Introduction ... 1
2 Methodology ... 2
3 Climate Change Adaptation and Education: A Conceptual Framework ... 4
4 Results: Risk to Climate-Related Disasters ... 8
4.1 Quantitative analysis of risk factorsdifferential vulnerability ... 8
4.1.1 Average levels of education in high- and low-risk areas... 8
4.1.2 Relationship between education and income ... 9
4.1.3 Disaster impacts and education ... 10
4.2 Qualitative analysis of risk factorsdifferential vulnerability ... 12
4.2.1 Education: Direct effect on aspects that reduce risk... 13
4.2.2 Education: Mitigating effects on aspects that increase risk ... 19
4.2.3 Disaster impacts on people’s education... 28
5 Discussion: Towards Sustainable Adaptation ... 30
5.1 The role of education for people’s adaptive capacities: Summary of key results 30 5.2 Comparative analysis: The climate and education nexus ... 31
5.2.1 Education and disaster risk ... 32
5.2.2 Institutional support for risk reduction and adaptation ... 32
5.2.3 Results with a ‘gender twist’ ... 33
5.3 From current risk reduction to sustainable adaptation... 34
5.3.1 Conceptual implications of results ... 34
5.3.2 Practical implications of results ... 37
6 Conclusions ... 37
7 References ... 38
Abstract
With a worldwide increase in disasters, the effects of climate change are already being felt, and it is the urban poor in developing countries that are most at risk. There is an urgent need to better understand the factors that determine people’s capacity to cope with and adapt to adverse climate conditions. This paper examines the influence of formal education in determining the adaptive capacity of the residents of two low- income settlements: Los Manantiales in San Salvador (El Salvador) and Rocinha in Rio de Janeiro (Brazil), where climate-related disasters are recurrent. In both case study areas it was found that the average levels of education were lower for households living at high risk, as opposed to residents of lower risk areas. In this context, the influence of people’s level of education was identified to be twofold due to (a) its direct effect on aspects that reduce risk, and (b) its mitigating effect on aspects that increase risk. The results further suggest that education plays a more determinant role for women than for men in relation to their capacity to adapt. In light of these results, the limited effectiveness of institutional support identified by this study might also relate to the fact that the role of formal education has so far not been sufficiently explored. Promoting (improved access to and quality of) formal education as a way to increase people’s adaptive capacity is further supported in respect to the negative effects of disasters on people’s level of education, which in turn reduce their adaptive capacity, resulting in a vicious circle of increasing risk.
Acknowledgments
Many thanks to Ebba Brink, Pasi Oskari Rantala, Mercedes Barillas and Carlos Umaña for their valuable input. Special gratitude to Luis, Jonas and Anna-Filippa for their inspiration and boundless support.
Funding for this work was made possible by the European Research Council (ERC) Advanced Investigator Grant focusing on Forecasting Societies’ Adaptive Capacities to Climate Change (ERC-2008-AdG 230195-FutureSoc).
About the Author
Dr. Christine Wamsler is Assistant Professor at The Lund University Centre for Sustainability Studies (LUCSUS); Visiting Professor at The Lund University Centre for Risk Assessment and Management (LUCRAM); External Lecturer at the Department of International Health at Copenhagen University, Denmark; and Honorary Fellow at the Institute for Development Policy and Management (IDPM) and the Global Urban Research Centre (GURC) at Manchester University, UK. In addition, she is working as a consultant for different organizations, such as the International Institute for Applied Systems Analysis (IIASA), Austria, and is part of the personnel pool of Risk Reduction Experts of the Swedish Civil Contingencies Agency (MSB).
She holds a PhD in the field of Urban Disaster Risk Management and Adaptation (Lund University), a Masters degree in International Humanitarian Assistance (University of Bochum, Germany), and post-graduate training in Evaluation (Centre for Evaluation CeVal, Germany), Emergency Management (Charles Sturt University, Australia), and Local and Community Level Disaster Risk Management (International Disaster Risk Management Centre IDRM, the Philippines). In addition, she has been trained as an Urban Planner and Architect (University Stuttgart, Germany;
Ecole d’Architecture Paris-Belleville, France).
Dr. Wamsler has vast experience in the field of sustainable urban development with a focus on climate change adaptation and disaster risk reduction. Focus countries include(d) Chile, Colombia, El Salvador, Mexico, the Philippines, Sweden, Togo, United Kingdom, and Tanzania. She has authored many articles on disaster management and adaptation and recently published a book on Urban Risk Reduction and Adaptation. She is the coordinator of the Master of Disaster Management Program at Lund University and responsible for four different Master courses at Lund and Copenhagen Universities on (a) disaster risk reduction, (b) disaster recovery, (c) sustainable recovery and climate change adaptation; and (d) methods for climate risk management.
Climate Change, Adaptation, and Formal Education:
The Role of Schooling for Increasing Societies’
Adaptive Capacities Christine Wamsler
with contributions from Ebba Brink, Pasi Oskari Rantala and Mercedes Barillas
1 Introduction
Today, climate change is on everyone’s lips. With the global temperature on the rise and a worldwide increase in so-called natural disasters, the effects of climate change are already being felt, and many of the current climate change studies predict a continued rise in the frequency of such events including windstorms, heat waves, heavy rains, floods and landslides (IPCC 2007). Each year, disasters trigger devastating losses in human lives and economical assets, with the poor in developing countries being most at risk (UNISDR 2002; Wisner et al. 2004).
With rapid urbanization which increasingly exposes populations and economies to climate-related hazards, the trend is for the risk to become urban (IPCC 2007). In Latin America and the Caribbean, 89 percent of the population is predicted to live in cities by 2050 (UN 2009). The urban poor, often living in informal settlements, on steep slopes or on flood plains, are particularly vulnerable (e.g., Bigio 2003; IPCC 2007;
Wamsler 2009; Wisner et al. 2004).
While considerable research has been conducted on many aspects related to the geological and biological impacts of climate change, little is known about the specific impacts on the future wellbeing of the world’s population and how it is related to our ability to adapt to changing climate conditions. In fact, knowledge about future societies’ adaptive capacities is one of the most important missing links in making predictions about the effects of climate change (Lutz 2008).
Against this background, this paper’s objective is to contribute to filling this gap by providing new knowledge on the aspects that shape people’s capacities to adapt to changing climate conditions. More specifically, the research presented in this paper aims to examine how the risk and adaptive capacity of the residents of two low-income settlements (Los Manantiales in San Salvador [El Salvador] and Rocinha in Rio de Janeiro [Brazil]) are influenced by their level of formal education. In addition, it analyzes the complex reality of people living in disaster-prone informal settlements or so-called ‘slums’, thus illustrating how their precarious living conditions and social marginalization are interlinked and, in turn, related to their level of formal education.
The motivation to focus on formal education is based on recent studies which hypothesize that educational attainment might enhance people’s ability to cope with
disasters (e.g., Adger et al. 2004; Toya & Skidmore 2005; Blankespoor et al. 2010).
Formal education refers here to studies at primary, secondary and university levels.1 After the description of the research methodology (second section), the third section presents the theoretical framework on which this study is based. The interrelations between the central concepts of disaster, risk, and adaptive capacity are identified and viewed from a holistic systems perspective of risk reduction and climate change adaptation. Linkages with formal education are also highlighted. The fourth section presents the research results resulting from the conducted quantitative and qualitative analyses. A comparison and interpretation of the different results is found in the fifth section, comparing the similarities, differences, and gaps between the outcomes from the two case study areas (in El Salvador and Brazil). Finally, the conclusions are presented.
2 Methodology
This paper is based on a comparative analysis of two case studies which examine the influence of formal education in determining the adaptive capacity of the residents of informal low-income settlements where climate-related disasters are recurrent. Both case studies were motivated by the project on Forecasting Societies’ Adaptive Capacities to Climate Change, funded by the European Research Council and coordinated at IIASA by Wolfgang Lutz (Lutz 2008).
The first case study was carried out in different phases between 2006 and 2011 and focuses on the community Los Manantiales in San Salvador, El Salvador, where flooding and landslides are the main hazards to life and livelihoods, followed by windstorms and earthquakes. Additional analyses were conducted in two other San Salvadorian communities: José Cecilio del Valle and Divina Providencia. The second case study was carried out between 2009 and 2011 in Laboriaux and Cachopa, two communities of Rocinha, an informal settlement in central Rio de Janeiro, Brazil where landslides and floods are recurrent.
In both case studies, in the following referred to as the San Salvador and the Rio case studies, data was collected through surveys, interviews, literature review and observation, and both statistical and qualitative data analyses were applied. The statistical analyses investigate how formal education influences people’s level of risk, their coping strategies, and the institutional support received. The qualitative analyses focus on exploring direct and secondary effects that education may have on disaster occurrence, and vice-versa.
1 The study’s focus on formal education does not imply that other forms of education or training are discarded as factors to the capacity to cope with disasters, but is rather a pragmatic measure to delimit the research (cf. Lutz 2008). If formal education would, indeed, be identified as a key factor to people’s adaptive capacity, this would support promoting formal education to sustainably assist people and communities at risk. In addition, it would facilitate forecasting the wellbeing of future populations, since demographic structures based on age and education are subject to slow change and therefore predictable for many decades ahead, which is rarely the case for other social, economic or institutional trends (Lutz 2008).
The surveys and interviews were mainly conducted during 2009–2011 and included 118 households in San Salvador and 94 households in Rio including families at high risk (i.e., the focus group of 92 and 49 households, respectively) and families at moderate risk (i.e., the control group of 26 and 44 households, respectively). In addition, around 90 interviews were held at different levels, including international and national risk management experts, organizational staff from organizations working in the case study areas, community leaders and other key informants. For the literature review, more than 200 publications were revised. In the context of the San Salvador case study, also institutional databases could be accessed and analyzed, and data of previous research collected during 2006 could be drawn from. The two case studies were finally followed up by desk work in 2011 to assess the different outcomes.
For the qualitative data analyses, a combination of literal reading, grounded theory (Glaser & Strauss 1967), systems analysis (Sterman 2000) and cultural theory (Thompson et al. 1990) was applied. For the statistical analyses of the data obtained from the two case studies, so called cross-tabulations2 were conducted to identify potential relationships between different attributes, and their significance was tested using χ2 (Chi square) tests.3 Based on the research objectives, the attributes to be analyzed were chosen to be people’s:
Level of formal education;
Level of income;
Level of risk;
Impact from past disasters (i.e., previous disaster experience);
Local strategies used to cope with risk/disasters (i.e., so-called coping strategies);
The institutional support received to reduce and adapt to disaster risk;
Other possible key factors or attributes.
In addition, one linear regression analysis was carried out to identify any relationship between educational level and level of income, two log linear analyses4 to examine the interaction between some independent variables, and a t-test to assess if the averages of education of the focus and control groups are significantly different from each other.
For the San Salvador and the Rio case studies a total of 315 and 80 quantitative tests, respectively, were made. First, the results which were individually statistically
2 A cross-tabulation is a joint frequency distribution based on two (or more) categorical variables. Also known as contingency table analysis, this method of displaying distributions of cases on two or more variables is a commonly used tool for conducting pair-wise comparison.
3 A χ2-test is then applied to the joint frequency distributions to determine if the variables are statistically correlated (Michael 2001). The method was chosen with the objective of exploring the individual correlations between the specified attributes, as opposed to, for instance, trying to appreciate the risk based on a combination of these attributes.
4 Log-linear analysis allows the user to test the different factors that are used in the cross-tabulation (e.g., gender, etc.) and their interactions for statistical significance (StatSoft, Inc. 2011).
5 20 tests with the dataset from 2009/10, an additional 6 tests with an existing institutional database from 2003, and an additional 5 tests with another institutional database from 2005. Only the analysis of the
significant with a 5 percent confidence level were identified.6 In the following, a Bonferroni type adjustment7 was performed to adjust the confidence level since the error probability increases with the number of tests conducted. In the following text, probabilities (before and after Bonferroni type adjustment) are indicated behind each result where appropriate (e.g., p0.003, adjusted p0.16). In some cases results with lower probability are included in order to highlight findings which are considered to be crucial to follow up in future studies.
To obtain a good approximation of ‘reality’, and thus reliability, and to deal with threats to the validity of the conclusions, like bias in the selection of cases, focus areas and self-report bias by the interviewees, different types of triangulation were used.
These include data, methodological, theoretical and investigator triangulation (cf.
Harvey & MacDonald 1993; Flick 2006). Remaining limitations are mainly due to the methods chosen for statistical analysis; differences in the context and approaches used for the two case studies; lacking historical data; and the very difficult access to existing data in the precarious and insecure study areas.
3 Climate Change Adaptation and Education: A Conceptual Framework
Disasters are commonly seen as the result of an interaction between so-called natural hazards (H) and vulnerable conditions (V). In other words, it is understood that hazards such as floods, landslides and windstorms do not cause disasters on their own. It is only when they are combined with vulnerable conditions, such as people or systems susceptible to the damaging effects of these hazardous events, that disasters do occur;
that is: “a serious disruption of the functioning of a community or a society involving widespread human, material, economic or environmental losses and impacts, which exceeds the ability of the affected community or society to cope using its own resources” (UNISDR 2009:9).
On this basis, disaster risk is conventionally expressed in the following pseudo- equation:
R = H · V (1)
where R stands for risk, H for hazard(s) and V for vulnerability.
While a disaster is said to be the result of “insufficient capacity or measures to reduce or cope with potential negative consequences” (UNISDR 2009:9), the definition of disaster risk (as represented by Eq. 1) does not include such capacities and/or
2003 database was considered of relevance, showing the situation before program implementation by La Fundación Salvadoreña de Desarrollo y Vivienda Mínima (FUNDASAL), El Salvador.
6 Meaning that the probability (p) for erroneously finding a correlation is at most 5 percent.
7 The Bonferroni type adjustment calculates for a confidence level a and a number of tests n the confidence level for the entire set of tests as a/n (Goldman 2008). The different error rates allowed in this study were a = 0.05, a = 0.10 and a = 0.16. The individual probability (p) of each result was compared with the adjusted error rate (A). If p < A, the result is significant at the corresponding level a (meaning that the probability (p) for erroneously finding a correlation is at most 5 percent, 10 percent, and/or 16 percent).
measures, and consequently does not link the components of risk to appropriate risk reduction measures. In addition, actions related to recovery are often not mentioned as an inherent part of risk reduction.8 However, preparedness for recovery is crucial for risk reduction since (a) both spontaneous and planned early recovery starts the moment a hazard occurs; (b) risk areas affected by a hazard are generally still in the process of recovering from earlier hazards; and (c) the term ‘hazard’ includes primary and secondary hazards (e.g., landslides or cholera after earthquakes and floods), and includes not only rapid but also slow-onset events which can develop over time or are successive (e.g., aftershocks) (Wamsler 2010).
The identified limitations led to the development of the extended definition of risk and risk reduction by Wamsler (2009) which directly links the different risk components to the corresponding risk reduction measures. These include not only measures of prevention (to reduce or avoid hazards), mitigation (to reduce vulnerability), and preparedness to respond (to improve post-disaster response), but also measures of preparedness to recover (to improve post-disaster recovery). This can be expressed by:
R = H/P · V/M · LR/PP (2)
where R stands for risk, H for hazard(s), V for vulnerability, LR for lack of mechanisms and structures to respond and recover, P for prevention, M for mitigation, and PP for preparedness for response and recovery.
The development of the extended risk definition has both theoretical and practical implications since the way risk is defined dictates how risk reduction is addressed (Slovic 1999). Notably, the four measures included in the extended risk definition are defined in a way to highlight that, for each type of measure, there are always two different ways to assist people to cope with or to adapt to changing climate conditions.9 These are (a) directly reducing the corresponding risk component, or (b) increasing capacities to reduce the corresponding risk component, thus enabling societies to reduce their level of risk on their own. In both cases, the active participation of institutions and people at risk and the building on local patterns of behavior and existing coping strategies proved to be crucial for achieving sustainable change (Wamsler 2007). The latter includes evaluating the local strategies for reducing risk, supporting and improving effective ones, scaling down unsustainable practices and, where necessary, offer better alternatives.
8 Only recently has it become more and more accepted by scholars and practitioners to include actions related to recovery in the notion of preparedness (cf. UNISDR 2009).
9 The definitions are as follows: Prevention (or hazard reduction) aims (to increase the capacity) to avoid or reduce the potential intensity and frequency of existing or likely future hazards that threaten households, communities, and/or institutions. Mitigation aims (to increase the capacity) to minimize the existing or likely future vulnerability of households, communities, and/or institutions to potential hazards/disasters. Preparedness for response aims (to increase the capacity) to establish effective response mechanisms and structures for households, communities, and/or institutions so that they can react effectively during and in the immediate aftermath of potential future hazards/disasters. Preparedness for recovery aims (to increase the capacity) to ensure appropriate recovery mechanisms and structures for households, communities, and/or institutions that are accessible after a potential hazard/disaster (including risk transfer and sharing).
Coping capacity is defined by UNISDR (2009:8) as “[t]he ability of people, organisations and systems, using available skills and resources, to face and manage adverse conditions, emergencies or disasters”. In other words, it includes already used coping capacities (i.e., existing coping strategies) as well as potential, but so far unused, coping capacities. The skills and resources mentioned in this definition of coping capacity can be translated into the four risk reduction measures of Eq. (2), which would mean that a system’s or people’s coping capacity is their ability to reduce their overall risk by applying these measures. While the term ‘adaptive capacity’ is not included in UNISDR’s glossary (2009), a definition can be found in the introduction to the IPCC Fourth Assessment Report, stating that “adaptive capacity is the ability of a system to adjust to climate change (including climate variability and extremes) to moderate potential damages, to take advantage of opportunities, or to cope with the consequences” (IPCC 2007:21). On this basis, and using the extended definition of risk described above, it can be assumed that people’s adaptive capacity and people’s coping capacity are determined by the same attributes or factors. Adaptive capacity and coping capacity are therefore used as synonyms in this study, as well as the associated process of increasing these capacities, namely, risk reduction and climate change adaptation.
Against this background, what are the key factors to people’s capacity to cope with and adapt to increasing disasters? Income is often considered as theor one of thekey factor (e.g., Blankespoor et al. 2010; Cutter et al. 2003; UN-HABITAT 2010;
Kahn 2005; Lindell & Perry 2004; Toya & Skidmore 2005; Wisner et al. 2004). It is argued that people who have resources (e.g., wealth, assets, insurance) are more likely to succeed in safeguarding their lives, property and livelihoods as well as make a swifter recovery after disasters, although their economic losses in disasters are often of greater magnitude in absolute numbers (Wisner et al. 2004). In contrast, education is generally not considered to be a key factor to people’s level of risk or their capacity to cope with and adapt to disasters. In fact, higher levels of education are generally only linked to a higher socioeconomic status and more lifetime earnings (e.g., Cutter et al. 2003). In other words, it is argued that it is only through its correlation with income that education is related to risk. In the context of models such as the Pressure and Release (PAR) Model10 and the ‘Sustainable Livelihoods (SL) approach’11 education is mentioned as one of many factors that people use to obtain a livelihood, thus contributing to their capacity to cope with stress and shocks (including disasters and other climate-related impacts) (Wisner et al. 2004).
10 The PAR Model seeks to explain the progression that leads to vulnerability by seeing it as a chain of three stages: root causes, dynamic pressures and unsafe conditions. Root causes are the most widespread and general (global) processes in society, such as ideologies and economic and political structures. These produce the dynamic pressures, which are more contemporary or direct conditions, such as deforestation, violent conflict or rapid urbanization. The dynamic pressures then ‘translate’ the root causes into unsafe conditions where people (on local, community or household levels) are prompted to interact with hazards, for example, having to live in dangerous locations or engage in unsafe activities to earn a living, being subject to precarious construction standards or lacking proper disaster preparedness (Wisner et al. 2004).
11 The SL approach is a model, promoted for instance by the UK foreign aid ministry, that seeks to explain how people obtain a livelihood by drawing on five types of capital: human capital (skills, education, health), social capital (networks, groups, institutions), physical capital (infrastructure, technology, equipment), financial capital (savings, credit) and natural capital (natural resources, land, water). A livelihood is considered sustainable when it can “cope and recover from stress and shocks”
(Wisner et al. 2004:94-95).
In recent studies, however, the question is raised as to whether formal education might in fact play a more central role in determining people’s adaptive capacity. Adger et al. (2004), Toya & Skidmore (2005) and Blankespoor et al. (2010) demonstrate, for instance, how a lower level of formal education, independent of income, is correlated with increasing numbers of deaths or other forms of loss from disasters, by combining different indicators of education with data from the OFDA/CRED International Disaster Database. In New Indicators of Vulnerability and Adaptive Capacity, Adger et al.
(2004:101) conclude that education exhibits “a strong [negative] relationship with mortality from climate related disasters”. Among the education proxies, the strongest indicator is the literacy rate among citizens aged 15-24, followed by the literacy rate among all citizens over 15, and the female to male literacy ratio. Toya & Skidmore (2005) use data on the total years of schooling attainment for the population aged 15 or over in Economic Development and the Impacts of Natural Disasters, and are able to demonstrate that countries with a higher number of years of schooling suffer less disaster-related deaths as well as damages per GDP. The correlation is particularly strong for developing countries for which the level of formal education proves more significant to disaster losses than for income levels. In The Economics of Adaptation to Extreme Weather Events in Developing Countries, where the female educational enrolment rate is used as an indicator, Blankespoor et al. (2010) establish that countries that invest in female education suffer less disaster-related deaths. Summarized, these studies are a strong indicator that formal education, as well as gender equality in education, seems to play a more important role in determining people’s level of risk than what has been previously considered. The studies presented focus on different aspects related to education and risk, such as access to information,12 understanding of risk,13 decision making,14 and the empowerment of women,15 but lack a more comprehensive analysis of the importance of education versus the different aspects or components that form a part of people’s level of risk.
12 As pointed out by Adger et al. (2004), literacy plays an important role in determining access to information about the urgency of adaptation to climate change and the assistance that will be offered by governments.
13 According to Adger et al. (2004), formal education is the basis for a ‘scientific’ understanding of the world and provides a foundation for understanding the complex nature of hazards and how to respond to them. Toya & Skidmore (2005) argue that citizens with higher education are able to make better choices regarding safe construction practices and location decisions. Several studies suggest that low educational attainment makes people generally less likely to understand or respond to warnings (Cutter et al.2003) and/or obey evacuation instructions (Lindell & Perry 2004).
14 Education is said to be a fundamental determinant of poverty and marginalization (e.g., Adger et al.
2004; UNDP 2004). With basic literary and numeric skills, it is argued that people have more means to become engaged in their society and be a part of the decision-making processes, including risk governance (UNDP 2004). Adger et al. (2004) also points out that people with low levels of education are less likely to have a political vote and their welfare is therefore often of low priority for governments. On a national and international level, some researchers argue that a higher educational attainment could be an important asset for finding new solutions for how to tackle the adverse effects of climate change.
According to UNDP (2004), a more educated population will, for instance, be better able to partner with experts in designing ways of protecting urban neighborhoods and rural communities.
15 Educating girls and women, thus promoting the empowerment of women, has been found to be one of the major determinants, if not the major determinant, of sustainable development (Blankespoor et al.
2010). For instance, educated women tend to have less children (e.g., Busso 2002), and a smaller number of dependents can in turn make families less vulnerable to hazardous impact (Cutter et al. 2003).
4 Results: Risk to Climate-Related Disasters
The conceptual framework presented in the previous section directly links to the research objectives of this study, which analyze the role of formal education as regards (a) people’s level of risk, (b) their coping strategies, and (c) the institutional support they receive. According to the extended view of risk, both the local strategies and the institutional support to cope with and adapt to climate-related disasters form part of the risk reduction measures available at the community and household level and thus belong under the denominator in the extended risk equation (see Eq. 2). In other words, the second and the third research objective are in fact part of the first research objective, since local coping strategies and institutional support are part of the factors that determine people’s level of risk. The difference in the factors that influence people’s level of risk can also be called differential vulnerability.
4.1 Quantitative analysis of risk factorsdifferential vulnerability
This section presents the results of the quantitative analyses of the factors that influence people’s differential vulnerability. The following four datasets formed the basis of the analyses:
(a) Survey data from the San Salvador case study (carried out in 2009/10);
(b) Survey data from the Rio case study (carried out in 2010/11);
(c) Institutional database of the low-income settlement Los Manantiales in San Salvador;
(d) Institutional database of the low-income settlement Divina Providencia in San Salvador.16
4.1.1 Average levels of education in high- and low-risk areas
The analysis of all four datasets indicates lower levels of education for households living at high risk as opposed to residents of lower risk areas. In other words, a correlation was identified between people’s level of education and people’s level of risk. Tables 1 and 2 show the comparison of the average number of years of schooling of the focus and control groups, considering both the average level of education of the heads of households and the average level of all (adult and working) household members.17,18
16 Divina Providencia forms part of the San Salvador case study and was included in the survey from 2009/10 (cf. Section 2).
17 The average levels of education of the family member with the highest level of education could also be compared. In San Salvador, the results were an average of 9.4 years of schooling for the low risk areas and 9.0 years of schooling for the high risk areas, while in the Rio case study an average of 9 years was identified for both the focus and the control group.
18 Allowing a 10 percent error rate, in the case of Rocinha only the difference between the (lower) average levels of education of the heads of households in the high risk area Laboriaux versus the (higher) averages in the low-risk area Cachopa proved to be significant (p0.082). T-tests were conducted to analyze if the differences in averages are statistically significant. The fact that none of the differences was statistically significant at the 5 percent level can be explained by the fact that a larger number of people would be needed in each group to prove that the identified differences did not happen by chance.
Table 1. Average education and income levels in focus and control groups (i.e., people living in high-risk and lower-risk areas) based on recent surveys.
Average education of head of household
(years)
Average education
of household
members (years)
Average income of
head of household
(US$ / BRL)*
Total income of household (US$ / BRL)*
Household income per
person (US$ / BRL)*
San Salvador case study
High risk
5.0 ** 6.2 111 243 57
Low risk
5.7 *** 7.0 71 259 59
Rio case study
High risk
5.6 6.5 818
(US$ 485) 1258
(US$ 746) 442 (US$ 262) Low
risk
7.0 7.1 801
(US$ 475) 1478
(US$ 876) 568 (US$ 278)
* US$ for San Salvador (local currency); BRL for Rio.
** If only those who receive income are included, the average is 6.5.
*** If only those who receive income are included, the average is 7.3.
Table 2. Average education and income levels in focus and control groups (i.e., people living in high-risk and lower-risk areas) based on analyses of institutional databases.
Average education of head of household
(years)
Average education
of household
members (years)
Average income of
head of household
(US$)
Total income of household
(US$)
Household income per
person (US$)
San Salvador Manantiales
(2003)
High risk
5.0 5.8 181 269 60
Low risk
5.8 6.3 171 288 74
San Salvador
Divina Providencia
High risk
2.1 3.0 64 143 39
Low risk
4.4 5.0 86 92 49
4.1.2 Relationship between education and income
Based on the results presented in the previous section, it was considered of interest to determine whether there is a direct relationship between education and income in the study areas. Thus, the average levels of income of the focus and control groups were
analyzed. In addition, a series of cross-tabulations, χ2 tests and a linear regression analysis were conducted.19
As opposed to the analyses of the average levels of education, the analysis of the average levels of income of the four datasets does not show a clear tendency and, thus, no clear correlation could be identified between people’s levels of income and people’s level of risk. In fact, Tables 1 and 2 show higher average incomes of the heads of households living in high risk areas (as opposed to those living in low risk areas) for three out of the four datasets. Looking at the average income per household member, the analysis of all four databases indicates a slightly higher income of those family members at lower risk. However, in the case of the San Salvador survey (Table 1), the difference of only US$ 2 is negligible. This result is confirmed with the linear regression analysis of the San Salvador survey which shows no relationship between income and education. However, the 2003 database of Los Manantiales shows a significant20 correlation between (a) the average educational level of those over 18 years of age and the total household income (p0.001; adjusted p0.05), and (b) the total household income and the educational level of the head of the household (p0.002;
adjusted p0.10). The database of Divina Providencia did not allow similar analyses.
In the Rio case study area, the cross-tabulation and χ2 tests did not show any significant correlation between education and income at the household level. However, for the female residents a significant correlation was identified between their level of education and income (p0.003; adjusted p0.16). No such correlation could be found for men. In other words, in the study area and only for women, it is very likely that a higher educational level leads to a higher income. While a similar analysis was not possible in the context of the San Salvador case study, the data analysis shows that the two most educated women (13 grades or higher) have a higher average income (i.e., US$ 325) than the men at the same educational level (i.e., US$ 207). In addition, the least educated women earn on average considerably less than the least educated men.
Finally, allowing an error rate of 10 percent, in the Rio case study none of the differences in income proved to be significant.21
4.1.3 Disaster impacts and education
To investigate the factors that influence people’s level of risk, different analyses were conducted to identify possible correlations between education and:
Impacts from past disasters;
Living in a (declared) risk area;
Knowledge of existing risk factors;
The use (and number) of coping strategies;
Institutional support received.
19 For the linear regression, average household monthly income was used as the response variable, and average household education in years as the independent variable.
20 Based on χ2 tests and Bonferroni type adjustments.
21 In the Rio case study, only by allowing a 32 percent error rate, a significant correlation could also be identified between the educational level of the highest educated person in the household and the total household income (p < 0.004; adjusted p < 0.32).
Analyses were made in order to analyze correlations between (a) the impact levels of past disasters; (b) past disaster impacts and the use of coping strategies; and (c) people’s own risk evaluation and assistance received to reduce risk. Related results are presented below.
Factors influencing past disaster impacts
A series of cross-tabulations was conducted to identify a possible correlation between people’s level of education and the way they were affected by past disasters. In the context of the San Salvador case study, additional cross-tabulations were conducted to assess whether previous disaster experience has an effect on households’ level of disaster risk (comparing the impact levels of Hurricane Mitch and the impact levels of Hurricane Stan).
Importantly, the analysis of the 2003 database of Los Manantiales shows a correlation between the educational level of the heads of household and disaster risk (p0.0015; adjusted p0.10),22 while no correlation could be found between income levels and disaster risk. Even by allowing a 16 percent error rate, the analyses of the other databases of the San Salvador and the Rio case studies did not indicate any correlation between income or education and past disaster impacts. However, in the San Salvador case study, a clear correlation could be found between the way in which households were affected by Hurricane Mitch in 1998 and the way in which the same households were affected by Hurricane Stan in 2005 (p0.001; adjusted p0.05).23 No such analysis could be made for the Rio case study. Here, data pointed toward a possible correlation between a lower mean educational level of households and living in a high-risk area (i.e., in Laboriaux) (p<0.005, adjusted p<0.4).
Factors influencing people’s way of coping
To assess if people’s use of coping strategies is influenced by their level of education, income, and/or past disaster impacts, cross-tabulations were performed using these variables.
The San Salvador case study did not show any significant correlation between education or income and (conscious) strategies taken to cope and adapt to (increasing) disaster risk. However, the analyses indicate a significant correlation between past disaster impacts and the use of coping strategies (p0.001; adjusted p0.05). In other words, those households who in the past were affected the most were also most likely to take risk reduction measures into their own hands (76.9 percent for Mitch; 88.2 percent for Stan).24 To further explore this relationship, another cross-tabulation was applied
22 Only significant at 10 percent after Bonferroni type adjustments using six as total number of analyses made.
23 68.8 percent of those households which were not affected by Hurricane Mitch in 1998 were also not affected by Hurricane Stan in 2005; 82.6 percent of those households which were affected only a little by Hurricane Mitch in 1998 were also affected a little by Hurricane Stan in 2005; and 83.3 percent of households affected a lot by Hurricane Mitch in 1998 were also affected a lot by Hurricane Stan in 2005.
24 The next most likely were those households that were affected a little (71.4 percent for Mitch; 67.4 percent for Stan). Households that were not affected were the least likely to take disaster risk reduction measures (41.2 percent for Mitch; 8.3 percent for Stan).
using three variables: household level of income, coping strategies taken, and past disaster impact. As a result, a significant correlation was found indicating that those in the high-income group (US$ 201 or more) were found to be more likely to take coping strategies to lessen their disaster risk than those in the low-income group (0–200 US$) (p0.0023; adjusted p0.10 for Mitch / p0.0001; adjusted p0.005 for Stan).
In the Rio case study, the level of education could be tested against (a) whether or not households had (consciously) taken coping strategies to reduce their level of risk, and (b) the number of reported coping strategies. The result was the identification of a significant correlation between the educational level of the interviewee and his or her ability to mention any types of risks in the settlement (p<0.00013, adjusted p<0.0104).
This was the most significant result of the Rio case study, meaning that interviewees with lower education were more likely to see their surroundings as risk-free, while those with higher education were more aware of existing risks. It was also found that interviewees with a higher level of education were able to point out a higher number of risks in the settlement (p<0.003, adjusted p<0.16). In the San Salvador case study, the survey data did not allow a similar comparison.25
Factors influencing institutional support
In order to assess if education, income, and/or past disaster impacts influence the institutional support households receive to cope with and adapt to disasters, a series of cross-tabulations was carried out. No significant correlations could be found. However, the data shows that in both case study areas the families living at high risk have received more institutional help than those at lower risk. Despite the institutional support received, out of these families 36 percent in the San Salvador case study and 63.3 percent in the Rio case study state that their current level of risk is similar or even worse than before. Additional analyses suggest that there might be a correlation between households being able to express being at risk and having received institutional support.
Allowing a 40 percent error rate, in Rocinha a significant correlation could be found between reporting to be at risk and having received institutional help (p<0.005, adjusted p<0.4).
4.2 Qualitative analysis of risk factorsdifferential vulnerability
This section presents the qualitative analyses of the factors that influence people’s differential vulnerability. The results show how disasters affect people living in informal settlements such as Los Manantiales and Rocinha, and how this is related to their level of formal education. In contrast to the quantitative analyses presented in the previous section, the qualitative analyses do not investigate the relative importance of education (as opposed to other factors such as income), but aim at providing illustrative examples of the kind of influence education can have on people’s level of disaster risk.
It thus provides an understanding of how education is linked to the conceptual framework presented in Section 2.
25 Here, interviewees only had to state if their level of risk is low, moderate, or high. On this basis, no correlation could be found between education and the answers.
4.2.1 Education: Direct effect on aspects that reduce risk
In both the San Salvador and the Rio case studies it was found that education can have a direct influence on people’s level of risk and associated risk reduction. Based on the comparison of data obtained from interviews, observation and relevant literature, formal education is considered to have a positive effect on people’s:
Awareness and understanding of existing risk;
Access to (and provision of) information on risk reduction;
Acceptance and adequate use of institutional support;
Way of coping (by improving their own risk reduction strategies).
As regards the latter, two issues that are related to formal education were identified to be of special relevance for efficient local coping. These are a) having a formal job, and b) people’s possibilities for (or interest in) moving to a lower risk area within or outside their settlement. The following sections describe these outcomes in more detail.
Awareness and understanding of existing risk
The statistical analyses of the Rio case study show a correlation between people’s level of education and their ability to perceive existing risks (see Section 4.1.3). The interviews with key informants and residents confirm this result. A representative of the Civil Defense of Rio de Janeiro states, for instance, that their work in Rocinha clearly shows that formal education is directly linked to people’s ability to perceive risks. With risk awareness being a necessary condition to engage in disaster risk reduction (UNISDR 2002), this demonstrates the vital role of education for people’s adaptive capacity.
In contrast, in the San Salvador case study—upon probing virtually all interviewees at high risk (i.e., 97 percent)—named either flooding or landslides as an imminent risk to their lives, and the majority (i.e., 83 percent of the focus group) could mention at least one factor that makes them more vulnerable (as compared to other residents living at lower risk) (Wamsler 2007). However, the qualitative analysis of the 2006 interviews shows that it was the illiterate interviewees at high risk who could not mention any additional risk factors.
Access to (and provision of) information on risk reduction
In both the Rio and the San Salvador case studies, observation and interviews with residents suggest that a higher level of education has a direct effect on people’s access to information. This includes information on existing:
Hazards and other threats;
Safer places to live;
Measures to reduce risk;
Knowledge on potential institutional support;
Laws and people’s own rights.
As an example, Ana, a highly educated female resident from Rocinha, mentions searching for risk information on the web as one of her main coping strategies (see Box 1). The improved access to information and the associated knowledge gained allows
people to create new ideas and to chase after opportunities to improve their situation.
Residents from Rocinha also suggested that better educated people have better means to express themselves, which is crucial for informing others (including authorities) about their own risk situation. In line with this, key informants state that people with higher levels of education are more likely to be successful in their contacts with authorities and emergency officials. A local community worker from Laboriaux states, for instance, that formal education often proves valuable for people at risk in their contact with emergency officials. This was also confirmed in the San Salvador case study, where those residents with the lowest levels of education were the ones who frequently mentioned that (a) they do not have any idea of how they could improve their situation, and (b) that they do not know of any institutions which could assist them.
Box 1. Access to information on risk reduction.
Ana, single mother, 40 years old, 11 years of education, is currently taking tests to enter university to study journalism. She lives in Cachopa. She has not received any institutional support to improve her situation, but she managed to get a stipend from the renowned and private language school Cultura Inglesa for her son to study English.
When asked about the ways she copes with existing disaster risk, she mentions a range of different strategies including:
- Looking for risk information on the internet;
- Investing in the structure of the house;
- Improving the electricity (distribution and outlets);
- Not throwing trash on the streets;
- Sending her son to study outside the favela (slum).
When asked about her interest in moving to another and more secure area, Ana states that there is a difference between living in a favela and being the favela (thus referring to the associated stigma of its residents), and then highlights that she only lives here because she does not have the possibility to live anywhere else.
At an individual level, increased awareness and understanding of existing risk, together with better access to and provision of information on risk reduction, are important preconditions to reduce existing risk by (a) accepting and/or adequately using institutional support, and (b) improving people’s own way of coping (see the following sections). At the community level, an unequal distribution of information on risk reduction was shown to create tension among residents, which negatively affects community-based risk reduction (Wamsler 2007).
Acceptance and adequate use of institutional support
The qualitative analyses of both case studies suggest that people with higher levels of education are more likely to be responsive to disaster warning and alerts (cf. Cutter et al. 2003; Lindell & Perry 2004). The Civil Defense of Rio de Janeiro informs that the negligence of their warnings and alerts is one of the principal reasons for the differential vulnerability of people living in the same community. A community worker residing in Laboriaux supports this, stating that education makes residents less suspicious towards the authorities and more likely to accept institutional support, if considered adequate. In the San Salvador case study, no such clear correlation could be found. Here, people from all educational levels were mentioning both the importance and the uselessness of emergency organizations such as emergency committees, fire fighters and the police.
However, observation suggests that people’s level of education influences people’s adequate use of institutional assistance (see Box 2). This refers to people’s active participation, maintenance of physical risk reduction measures, regular contributions to local emergency funds, and the adequate use of credits received. The latter is confirmed by a community leader who states that it is not the quantity, but the inadequate use of money which is a determinant for residents’ high level of risk.
Box 2. Acceptance and use of institutional support.
Francisca lives with her husband and her baby in the high-risk area Laboriaux. She is 26 years old and has 8 years of education. When asked how she copes with the imminent risk of landslides, she mentions a range of different strategies including staying at home in order to not miss any information from the Civil Defense Service.
*****
Maria, a female resident from Los Manantiales, 6 years of education, takes an active part in the community-based work offered by the institution FUNDASAL to reduce existing risk. While several interviewees expressed their reluctance to actively participate, she says: “It is true that we [meaning the poor] have to work [in order to reduce our risk], but this is how it is, we have to work hard if we really want to make a change here and have a better life.”26
*****
A technical staff member working in Rocinha for the governmental program, Programa de Aceleração do Crescimento, describes the importance of education:
“Facing a disaster, the affected families have a lot of issues to solve and to deal with.
Those who have a better education can generally cope better with the post-disaster situation than those who have less education, […] because education helps them to make better decisions, for instance, when they have to decide where to go to an emergency shelter, when they have to deal with authorities or other institutions which offer different types of assistance, etc. These are cases where better education will be of help. Hence, people’s education is certainly a determinant [for people’s level of risk].”27
26 Original citation: “Es cierto que a nosotros nos toco trabajar, pero así tiene que ser algo que le cueste a uno, para vivir mejor.”
27 Original citation: “Visto a haver um desastre, as familias involvidas têm uma dificuldade a enfrentar.
Então os que têm melhor educação podem enfrentar melhor dos que têm menos, […] porque a educação
Improvement of own coping strategies
In both study areas, it was only after probing that around 65 percent of the interviewees could mention any kind of strategies or improvements made to reduce their risk.
However, observation and interviews with key informants show that virtually all the residents in Los Manantiales and Rocinha are actively adapting to their risk situation, which is a common feature in many southern low-income settlements (cf. Jabeen et al.
2009; Wisner et al. 2004, 2007). The strategies the residents are aware of, and thus are consciously applying, are mainly of a structural or economic nature (such as improvements in their houses and the surrounding areas, saving money, taking credits).
After probing, it is mainly those interviewees with a higher level of education who mention, and actively use other types of strategies. These include strategies that are directly related to education, such as:
Sending children to study outside their own settlement (see Box 3);
Temporarily sending children to study outside their own settlement (only after disaster occurrence) (see Box 3);
Improving physical access to school (e.g., cementing streets so that children do not sink into the mud, or building small wooden bridges where landslides washed away parts of the street);
Encouraging dependents to study;
Taking a job outside their own settlement;
Being able to change one’s employer (e.g., depending on changing demands which can also be influenced by climate variability and extremes);
Staying constantly informed about existing risk (by using different sources).
Box 3. Improved coping through educationeducation as a conscious strategy.
Ana, single mother, 11 years of education, lives in Cachopa. When asked how she deals with existing risks, she mentions sending her son to study outside the favela so that his education is not affected by the problems within the favela, including natural hazards, shootings, power cuts, striking teachers, etc. In contrast, Francisca, single mother, 8 years of education, living in Laboriaux, was sending her two eldest sons to the local school. However, after the devastating landslides in 2010 and the resultant closure of the local school, she decided to send them to her mother. Francisca mentions this as an active strategy to cope with the recent disaster. She highlights that she does not want her boys to miss any classes and that she is afraid she will not be able to run out of her house with her two boys and her baby in case of another landslide.
Data suggests that it is not necessarily the number of strategies, but the use of different types of strategies that differs between people of different educational levels.
This increases the likelihood of tackling not only one, but several different risk components (i.e., existing hazards, vulnerabilities, response mechanisms and recovery
possibilita tomar decisões mais acertadas. Num caso de recorrer um abrigo, de acionar um poder público, ou acionar um outro poder, uma outra ajuda; a educação vai contribuir. Então com certeza a educacão é determinate.”
mechanisms). In contrast to education, the qualitative analysis shows that increased income often leads to an increased number of, or focus on, physical improvements, which does not necessarily lead to reduced risk (Wamsler 2007). Lost efforts due to destroyed contention walls and embankments were frequently mentioned. In addition, better-off households are more likely to opt out of community engagement, which can have a negative effect not only on social cohesion but on the disaster resilience of the entire community (Wamsler 2007).
Finally, two education-related issues were identified to be of special relevance for efficient local coping: having a formal job and people’s interest in moving to a lower risk area (within or outside the settlement). The following sub-sections explain their potential to reduce risk and how they are related to people’s level of education.
Possibilities of attaining a formal job
Neither the qualitative analysis of the San Salvador study nor the Rio case study indicates a strong correlation between formal education and income. However, both studies show the importance of having a formal job for coping with disasters. In fact, supporting dependents to obtain a formal job is part of people’s coping strategies (Wamsler 2007). Interviewees state that a formal job allows them an easier and/or cheaper access to:
Post-disaster credits (directly from employers or from banks, etc.);
Life insurance (for family dependents left behind);
Pension after retirement or in case of the inability to work;
Secure income (e.g., job not vulnerable to climate variables and extremes);
Health insurance (allowing better and cheaper treatment) (see Box 6);
Possibility to take (paid) sick leave (e.g., after disasters);
Other workers’ benefits (such as a 13th salary, regulated hourly rates, staff security regulations/equipment);
Direct post-disaster assistance from employers (such as construction materials);
An official address (of the employer) required to register children at school.
The importance of these issues is shown in the case of an informal worker living in Divina Providencia who pays into the social security system through deals with entrepreneurs who certify his employment, thus getting (illegal) access to formal insurance mechanisms.28 In addition, people working in the informal sector often need to work at several jobs and, thus, have little time left over for community-based efforts to reduce risk (Wamsler 2007). Finally, interviewees suggested that the level of formal education is a determinant for people obtaining a job in the formal sector (see Box 4), and that the correlation between formal education and income is less likely for male residents. The latter relates to the fact that there are more well-paid jobs for men (than for women) that do not require any formal education (such as motorcycle taxi driver and bartender).
28 Note that in the San Salvador case study only 26 out of the 331 residents included in the study had access to the Salvadoran social security system. The low number not only reflects the low number of residents having a formal job, but also the fact that employers take advantage of low-income people by offering formal jobs without any formal benefits.
